Whole thing
2023-04-24
# Libraries
library(tidyverse)
library(dplyr)
library(haven)
library(ggplot2)
library(tidyr)Import data
Read in the dataset.
df <- read_sav("~/Desktop/Coding/data/Mothership_DV.sav")
Mothership<-select(df,ID1:Sexuality_1,MDD_C:Day40_CUXOS,SUMdep_0:SUManx_1,SUMdep_DC,SUManx_DC,BL_CUDOS, BL_CUXOS)
view(Mothership)Rename and Recode variables
We can change the current variables to make them easier to interpret.
#recode varaibles
Mothership <- mutate(Mothership,
DC_status_1 = as.factor(DC_status_1),
DC_status_1 = recode(DC_status_1,
"1"="Complete",
"2"="Nearly Complete",
"3"="Insurance",
"4"="Nonatteneance",
"5"="Inappropriate",
"6"="Inpatient",
"7"="Withdrew",
"8"="Dissatisfaction",
"9"="Other"),
Education_1 = as.factor(Education_1),
Education_1 = recode(Education_1,
"1"="<Grade 12",
"2"="HS Diploma",
"3"="GED",
"4"="Some College",
"5"="Associates Degree",
"6"="Bachelors Degree",
"7"="Some Grad School",
"8"="Grad Degree"),
Gender_1 = as.factor(Gender_1),
Gender_1 = recode(Gender_1,
"0"="Female",
"1"="Male",
"2"="Non-Binary",
"3"="Other",
"4"="Unknown"),
Relationship_1 = as.factor(Relationship_1),
Relationship_1 = recode(Relationship_1,
"0"="Married",
"1"="Cohabitating",
"2"="Widowed",
"3"="Separated",
"4"="Divorced",
"5"="Never Married"),
Sexuality_1 = as.factor(Sexuality_1),
Sexuality_1 = recode(Sexuality_1,
"1"="Straight",
"2"="Gay",
"3"="Bisexual",
"4"="Other"),
IPT_track_1 = as.factor(IPT_track_1),
IPT_track_1 = recode(IPT_track_1,
"0"="General",
"1"="Trauma",
"2"="Young Adult",
"3"="BPD"),
Race_1 = as.factor(Race_1),
Race_1 = recode(Race_1,
"0"="White",
"1"="Black",
"2"="Hispanic",
"3"="Asian",
"4"="Portugese",
"5"="Other"),
Education_1 = as.factor(Education_1),
Education_1 = recode(Education_1,
"0"="<Grade 6",
"1"="Grades 7-12",
"2"="HS Diploma",
"3"="GED",
"4"="Some College",
"5"="Associates Degree",
"6"="Bachelors Degree",
"7"="Some Grad School",
"8"="Grad Degree"),
DC_status_1 = as.factor(DC_status_1),
DC_status_1 = recode(DC_status_1,
"1"="Treatment Complete",
"2"="Treatment Near Complete",
"3"="Insurance Stopped",
"4"="Nonattendence",
"5"="Inapproparite Language",
"6"="Transfered To Impatient",
"7"="Withdrew for External factors",
"8"="Withdrew (AMA)",
"9"="Other",
"10"="Other"),
DC_status_1 = as.factor(DC_status_1),
DC_status_1 = recode(DC_status_1,
"1"="Inpatient",
"2"="PHP",
"3"="APS",
"4"="Psychatrist",
"5"="PCP",
"6"="Another Physican",
"7"="Therapist",
"17"="Other"
),
#Lets make some new variables
#Duration of treatment
Duration = as.factor(
ifelse(Days_complete_1>0 & Days_complete_1 <=5, "1",
ifelse(Days_complete_1>5 & Days_complete_1 <=10, "2",
ifelse(Days_complete_1>10 & Days_complete_1 <=15, "3", "4"
)))),
Duration = recode(Duration,
"1"="1-5",
"2"="6-10",
"3"="11-15",
"4"="16+"
))
#Extract Year From Intake Date
#Convert to Date
library("lubridate")
Mothership$Intake_1<-ymd(Mothership$Intake_1)
Mothership$TxYear<-as.numeric(format(Mothership$Intake_1,"%Y"))
Mothership<-mutate(Mothership,
TxYear = as.factor(TxYear),
TxYear = recode(TxYear,
"1582"="2014",
"2021"="2020",
"2022"="2020"))Export as CSV
write.csv(Mothership,"~/Desktop/Coding/data/Mothership_Vis.csv")Background Statistics
### Bar Chart for Discharge Status ###
ggplot(data = subset(Mothership, !is.na(DC_status_1)), mapping = aes(x = DC_status_1))+
geom_bar(color = "black",fill="darkgreen")+
ggtitle("Frequency of Treatment Discharge Status") +
theme(axis.text.x = element_text(angle = 45,hjust = 1))+
xlab("Discharge Status") +
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
scale_y_continuous(name = "Frequency",limits=c(0,6000))
### Bar Chart for Education Status ###
ggplot(data = subset(Mothership, !is.na(Education_1)),mapping = aes(x = Education_1))+
geom_bar(color = "black",fill="darkred")+
ggtitle("Frequency of Education Level") +
theme(axis.text.x = element_text(angle = 45,hjust = 1))+
xlab("Education Level")+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
scale_y_continuous(name = "Frequency",limits=c(0,3000))
### Bar chart for race ###
ggplot(data = subset(Mothership, !is.na(Race_1)),mapping = aes(x = Race_1))+
geom_bar(color = "black",fill="darkblue")+
ggtitle("Frequency of Race/Ethnicity") +
theme(axis.text.x = element_text(angle = 45,hjust = 1))+
xlab("Race")+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
scale_y_continuous(name = "Frequency",limits=c(0,6500))
### Bar chart for gender###
ggplot(data = subset(Mothership, !is.na(Gender_1)),mapping = aes(x = Gender_1))+
geom_bar(color = "black",fill="darkorange")+
ggtitle("Frequency of Gender") +
theme(axis.text.x = element_text(angle = 45,hjust = 1))+
xlab("Gender")+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
scale_y_continuous(name = "Frequency",limits=c(0,6500))
### Treatment Track ###
ggplot(data = subset(Mothership, !is.na(IPT_track_1)),mapping = aes(x = IPT_track_1))+
geom_bar(color = "black",fill="skyblue")+
ggtitle("Frequency of Treatment Type") +
theme(axis.text.x = element_text(angle = 45,hjust = 1))+
xlab("Treatment Type")+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
scale_y_continuous(name = "Frequency",limits=c(0,6500))
### Treatment Year ###
ggplot(data = subset(Mothership, !is.na(TxYear)),mapping = aes(x = TxYear))+
geom_bar(color = "black",fill="#9350C7")+
ggtitle("Frequency of Patients per Year") +
theme(axis.text.x = element_text(angle = 45,hjust = 1))+
xlab("Treatment Year")+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
scale_y_continuous(name = "Frequency",limits=c(0,1500))
### Duration of Treatment ###
ggplot(data = subset(Mothership, !is.na(Duration)),mapping = aes(x = Duration))+
geom_bar(color = "black",fill="lightgreen")+
ggtitle("Frequency of Patients per Duration of Treatment") +
theme(axis.text.x = element_text(angle = 45,hjust = 1))+
xlab("Treatment Duration")+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
scale_y_continuous(name = "Frequency",limits=c(0,6500))
Pie Chart For Education Status
#Create Table and Data Frame
Education<-table(Mothership$Education_1)
Education_df<-as.data.frame(Education)
Education_df## Var1 Freq
## 1 <Grade 12 561
## 2 HS Diploma 1191
## 3 GED 473
## 4 Some College 2819
## 5 Associates Degree 776
## 6 Bachelors Degree 1351
## 7 Some Grad School 440
## 8 Grad Degree 922#Rename Columns
colnames(Education_df)[1]="Category"
colnames(Education_df)[2]="Frequency"
Education_df$Category=recode(Education_df$Category,
"0"="<Grade 6",
"1"="Grades 7-12",
"2"="HS Diploma",
"3"="GED",
"4"="Some College",
"5"="Associates Degree",
"6"="Bachelors Degree",
"7"="Some Grad School",
"8"="Grad Degree")
ggplot(data = Education_df, mapping = aes(x="", y=Frequency, fill=Category))+
geom_bar(stat="Identity",width=1,color="white")+
ggtitle("Education Level of Patients") +
scale_fill_discrete(name = "Education") +
coord_polar("y",start=0)+
theme_void()
Visualizing disorders
Create Diagnosis Variables
Mothership<-Mothership %>%
mutate(
# Mood Disorders
Mood_D = as.factor(
ifelse(
MDD_P ==1 | Bipolar1_P == 1 | Bipolar2_P == 1 | Dysthymia_P == 1, 1, 0)),
# Anxiety Disorders
Anxiety_D = as.factor(
ifelse(
GAD_P ==1 | Panic_P == 1 | PanicAgor_P == 1 | Agoraphobia_P == 1 |
Social_P == 1 | Specific_P == 1, 1, 0)),
# Obsessive Compusive Disorders
#OCD_P is for current OCD
# Personality Disorders
#BPD_P is for current BPD
# Substance Use Disorders
Substance_D = as.factor(
ifelse(
Alcohol_P == 1 | Drug_P ==1, 1, 0)),
# Trauma or stresor related disorders
Trauma_D = as.factor(
ifelse(PTSD_P == 1 | Adjustment_P ==1, 1,0)),
# Psychotic Disorders
Psychotic_D = as.factor(
ifelse(
Schiz_P == 1 | Schizaff_P == 1,1,0)),
# Eating Disorders
Eating_D = as.factor(
ifelse(
Anorex_P == 1 | Bulim_P == 1 | Binge_P == 1, 1,0)),
# Somatic Disroders
Somatic_D = as.factor(
ifelse(
Somat_P == 1 | UndiffSoma_P == 1| Hypochondriasis_P ==1,
1,0)),
Disorder_Type = as.factor(
ifelse(Anxiety_D == 1, "Anxiety",
ifelse(OCD_P == 1, "OCD",
ifelse(BPD_P == 1, "BPD",
ifelse(Substance_D == 1, "Substance Use",
ifelse(Trauma_D == 1, "Stress and Trauma",
ifelse(Mood_D == 1, "Mood",
ifelse(Psychotic_D == 1, "Psychotic",
ifelse(Eating_D == 1, "Eating",
ifelse(Somatic_D == 1, "Somatic", NA
)))))))))),
Anxiety_C = as.factor(
ifelse( GAD_C ==1 | Panic_C == 1 | PanicAgor_C == 1 | Agoraphobia_C == 1 |
Social_C == 1 | Specific_C == 1, 1, 0)),
Mood_C = as.factor(
ifelse(MDD_C ==1 | Bipolar1_C == 1 | Bipolar2_C == 1 | Dysthymia_C == 1, 1, 0)),
Substance_C = as.factor(
ifelse(Alcohol_C == 1 | Drug_C ==1, 1, 0)),
Trauma_C = as.factor(
ifelse(PTSD_C == 1 | Adjustment_C ==1, 1,0)),
Psychotic_C = as.factor(
ifelse(Schiz_C == 1 | Schizaff_C == 1,1,0)),
Eating_C = as.factor(
ifelse(Anorex_C == 1 | Bulim_C == 1 | Binge_C == 1, 1,0)),
Somatic_C = as.factor(
ifelse(Somat_C == 1 | UndiffSoma_C == 1| Hypochondriasis_C ==1, 1,0)),
#emotional disorders
Emotional_dis = as.factor(
ifelse(Anxiety_D == 1 | OCD_P == 1 | Trauma_D == 1 |
Mood_D == 1, "Emotional Disorder", "Other Disorder")),
Co_Anx = as.factor(
ifelse(Anxiety_D == 1 & Mood_C == 1 |
Anxiety_D == 1 & OCD_C == 1 |
Anxiety_D == 1 & BPD_C == 1 |
Anxiety_D == 1 & Substance_C == 1 |
Anxiety_D == 1 & Trauma_C == 1 |
Anxiety_D == 1 & Eating_C ==1 |
Anxiety_D == 1 & Psychotic_C ==1 |
Anxiety_D == 1 & Somatic_C ==1, 1, 0)),
Co_Mood = as.factor(
ifelse(Mood_D == 1 & Anxiety_C == 1 |
Mood_D == 1 & OCD_C == 1 |
Mood_D == 1 & BPD_C == 1 |
Mood_D == 1 & Substance_C == 1 |
Mood_D == 1 & Trauma_C == 1 |
Mood_D == 1 & Eating_C ==1 |
Mood_D == 1 & Psychotic_C ==1 |
Mood_D == 1 & Somatic_C ==1 , 1, 0)),
Co_OCD = as.factor(
ifelse(OCD_C == 1 & Anxiety_C == 1 |
OCD_C == 1 & Mood_C == 1 |
OCD_C == 1 & BPD_C == 1 |
OCD_C == 1 & Substance_C == 1 |
OCD_C == 1 & Trauma_C == 1 |
OCD_C == 1 & Eating_C ==1 |
OCD_C == 1 & Psychotic_C ==1 |
OCD_C == 1 & Somatic_C ==1, 1, 0)),
CO_BPD = as.factor(
ifelse(BPD_C == 1 & Anxiety_C == 1 |
BPD_C == 1 & Mood_C == 1 |
BPD_C == 1 & OCD_C == 1 |
BPD_C == 1 & Substance_C == 1 |
BPD_C == 1 & Trauma_C == 1 |
BPD_C == 1 & Eating_C ==1 |
BPD_C == 1 & Psychotic_C ==1 |
BPD_C == 1 & Somatic_C ==1 , 1, 0)),
CO_Psychotic = as.factor(
ifelse(Psychotic_D == 1 & Mood_C == 1 |
Psychotic_D == 1 & OCD_C == 1 |
Psychotic_D == 1 & BPD_C == 1 |
Psychotic_D == 1 & Substance_C == 1 |
Psychotic_D == 1 & Trauma_C == 1 |
Psychotic_D == 1 & Eating_C ==1 |
Psychotic_D == 1 & Anxiety_C ==1 |
Psychotic_D == 1 & Somatic_C ==1, 1, 0)),
CO_Eating = as.factor(
ifelse(Eating_D == 1 & Mood_C == 1 |
Eating_D == 1 & OCD_C == 1 |
Eating_D == 1 & BPD_C == 1 |
Eating_D == 1 & Substance_C == 1 |
Eating_D == 1 & Trauma_C == 1 |
Eating_D == 1 & Psychotic_C ==1 |
Eating_D == 1 & Anxiety_C ==1 |
Eating_D == 1 & Somatic_C ==1, 1, 0)),
CO_Somatic = as.factor(
ifelse(Somatic_D == 1 & Mood_C == 1 |
Somatic_D == 1 & OCD_C == 1 |
Somatic_D == 1 & BPD_C == 1 |
Somatic_D == 1 & Substance_C == 1 |
Somatic_D == 1 & Trauma_C == 1 |
Somatic_D == 1 & Eating_C ==1 |
Somatic_D == 1 & Anxiety_C ==1 |
Somatic_D == 1 & Psychotic_C ==1, 1, 0)))
##### df for plots
# df without NAs in date and disorder type
Mothership_Diag <- Mothership %>%
filter(!is.na(Disorder_Type))%>%
filter(!is.na(TxYear))
# df with and without depression
No_depressy <- Mothership_Diag %>%
filter(Disorder_Type != "Mood")Amount of disorders treated
#overall psychiatric disorders
ggplot(data = subset(Mothership_Diag, !is.na(Disorder_Type)),mapping = aes(x = Disorder_Type, fill=Disorder_Type))+
geom_bar()+
ggtitle("Frequency of Psychiatric Disorder Subtypes") +
theme(axis.text.x = element_text(angle = 45,hjust = 1))+
xlab("Psychiatric Disorder Subtype")+
theme(panel.grid.major.x = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
scale_y_continuous(name = "Frequency",limits=c(0,3500))+
coord_flip()
Number of disorders over time.
# Libraries
library(ggplot2)
#install.packages("hrbrthemes")
library(hrbrthemes)
library(dplyr)
library(tidyr)
library(viridis)
# Stacked
ggplot(data=subset(Mothership_Diag, !is.na(Disorder_Type)),
mapping= aes(x=TxYear, y = stat(count),
group=Disorder_Type, fill=Disorder_Type)) +
geom_density(adjust=1.5,alpha=.4,position="fill") +
theme(axis.text.x = element_text(angle = 45,hjust = 1))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
xlab("Treatment Year") +
theme(panel.background=NULL) +
facet_wrap(~Emotional_dis)+
labs(
title = "Type of Disorders Treated at the Hospital Per Year",
subtitle = "Broken Up By Disorder Types") +
xlab("Treatment Year") +
ylab("Percent of Patients With Disorder")
# Broken up by disorder.
ggplot(data=subset(Mothership_Diag, !is.na(Disorder_Type)),
mapping= aes(x=TxYear,y = stat(count),
group=Disorder_Type, fill=Disorder_Type)) +
geom_density(adjust=1.5,alpha=.6) +
facet_wrap(~Disorder_Type)+
theme_classic()+
theme(
panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2),
axis.text.x = element_text(angle = 45,hjust = 1),
legend.position="none",
panel.spacing = unit(0.1, "lines"),
axis.ticks.x=element_blank(),
panel.background=NULL) +
labs(
title = "Type of Disorders Treated at the Hospital Per Year",
subtitle = "Broken Up By Dmotional vs Other Disorders") +
xlab("Treatment Year") +
ylab("Number of Patients With Disorder")
# without depression
ggplot(data=subset(No_depressy, !is.na(Disorder_Type)),
mapping= aes(x=TxYear,y = stat(count),
group=Disorder_Type, fill=Disorder_Type)) +
geom_density(adjust=1.5,alpha=.6) +
facet_wrap(~Disorder_Type)+
theme_classic()+
theme(
panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2),
axis.text.x = element_text(angle = 45,hjust = 1),
legend.position="none",
panel.spacing = unit(0.1, "lines"),
axis.ticks.x=element_blank(),
panel.background=NULL) +
labs(
title = "Type of Disorders Treated at the Hospital Per Year",
subtitle = "Broken Up By Dmotional vs Other Disorders") +
xlab("Treatment Year") +
ylab("Number of Patients With Disorder")
Depression Treatment Progress Charts
Mothership_Long <- gather(Mothership,rm,CUDOS,Day1_CUDOS:Day40_CUDOS,factor_key = "T")
Mothership_Long <- Mothership_Long[order(Mothership_Long$ID1),]
#Create a Time Variable
Mothership_Long$TxDay <- NA
Mothership_Long$TxDay[Mothership_Long$rm=="Day1_CUDOS"]<-1
Mothership_Long$TxDay[Mothership_Long$rm=="Day2_CUDOS"]<-2
Mothership_Long$TxDay[Mothership_Long$rm=="Day3_CUDOS"]<-3
Mothership_Long$TxDay[Mothership_Long$rm=="Day4_CUDOS"]<-4
Mothership_Long$TxDay[Mothership_Long$rm=="Day5_CUDOS"]<-5
Mothership_Long$TxDay[Mothership_Long$rm=="Day6_CUDOS"]<-6
Mothership_Long$TxDay[Mothership_Long$rm=="Day7_CUDOS"]<-7
Mothership_Long$TxDay[Mothership_Long$rm=="Day8_CUDOS"]<-8
Mothership_Long$TxDay[Mothership_Long$rm=="Day9_CUDOS"]<-9
Mothership_Long$TxDay[Mothership_Long$rm=="Day10_CUDOS"]<-10
Mothership_Long$TxDay[Mothership_Long$rm=="Day11_CUDOS"]<-11
Mothership_Long$TxDay[Mothership_Long$rm=="Day12_CUDOS"]<-12
Mothership_Long$TxDay[Mothership_Long$rm=="Day13_CUDOS"]<-13
Mothership_Long$TxDay[Mothership_Long$rm=="Day14_CUDOS"]<-14
Mothership_Long$TxDay[Mothership_Long$rm=="Day15_CUDOS"]<-15
Mothership_Long$TxDay[Mothership_Long$rm=="Day16_CUDOS"]<-16
Mothership_Long$TxDay[Mothership_Long$rm=="Day17_CUDOS"]<-17
Mothership_Long$TxDay[Mothership_Long$rm=="Day18_CUDOS"]<-18
Mothership_Long$TxDay[Mothership_Long$rm=="Day19_CUDOS"]<-19
Mothership_Long$TxDay[Mothership_Long$rm=="Day20_CUDOS"]<-20
#Create a New Data Set with an Average Anxiety Variable
Mothership_Mean_CUDOS<-Mothership_Long %>%
group_by(TxDay) %>%
summarise(mean_CUDOS=mean(CUDOS,na.rm=TRUE))#Plot the Depression Mean for Full Sample
ggplot(data=Mothership_Mean_CUDOS,aes(x=TxDay,y=mean_CUDOS))+
geom_line(size=1,color="#0065A3")+
geom_point(color="#0065A3")+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
ggtitle("Mean Depression Severity During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20))+
scale_y_continuous(name="Depression Severity",limits=c(20,35,by=5))+
theme(panel.background=NULL) 
ggplot(Mothership, aes(Days_complete_1))+
geom_density()+
scale_x_continuous(limits=c(0,20,by=1)) +
labs(
title="Quantity of discharges Per Treatment Day")
plot means based upon duration group membership
#plot depression mean with each group
# New dataset for the treatment duration depression plot
Mothership_Mean_CUDOS<-Mothership_Long %>%
group_by(Duration,TxDay) %>%
summarise(mean_CUDOS=mean(CUDOS,na.rm=TRUE))
Mothership_duration_CUDOS<-Mothership_Mean_CUDOS[1:83,]
#delete weird Na values and put NA for values
Mothership_duration_CUDOS[6:20,3] = NA
Mothership_duration_CUDOS[37:41,3] = NA
Mothership_duration_CUDOS[74:83,3] = NA
Mothership_duration_CUDOS<-Mothership_duration_CUDOS[(-21),]
Mothership_duration_CUDOS<-Mothership_duration_CUDOS[(-41),]
Mothership_duration_CUDOS<-Mothership_duration_CUDOS[(-61),]
Mothership_duration_CUDOS## # A tibble: 80 × 3
## # Groups: Duration [4]
## Duration TxDay mean_CUDOS
## <fct> <dbl> <dbl>
## 1 1-5 1 31.1
## 2 1-5 2 24.0
## 3 1-5 3 21.3
## 4 1-5 4 18.6
## 5 1-5 5 16.1
## 6 1-5 6 NA
## 7 1-5 7 NA
## 8 1-5 8 NA
## 9 1-5 9 NA
## 10 1-5 10 NA
## # … with 70 more rowsggplot(data=Mothership_duration_CUDOS,aes(x=TxDay,y=mean_CUDOS,group=Duration,color=Duration))+
geom_line(size=1)+
geom_point()+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
ggtitle("Mean Depression Severity During Partial Hospitalization")+
labs(subtitle = "Broken up by Discharge Date")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20))+
scale_y_continuous(name="Depression Severity",limits=c(15,40,by=5))+
scale_color_manual(values=c("skyblue","#0065A3",
"navy","blue"))+
theme(panel.background=NULL)
Depression Treatment Progress by Characteristics
Race
Mothership_Long_dep <- gather(Mothership,rm,CUDOS,Day1_CUDOS:Day40_CUDOS,factor_key = "T")
Mothership_Long_dep <- Mothership_Long_dep[order(Mothership_Long_dep$ID1),]
#Create a Time Variable
Mothership_Long_dep$TxDay <- NA
Mothership_Long_dep$TxDay[Mothership_Long_dep$rm=="Day1_CUDOS"]<-1
Mothership_Long_dep$TxDay[Mothership_Long_dep$rm=="Day2_CUDOS"]<-2
Mothership_Long_dep$TxDay[Mothership_Long_dep$rm=="Day3_CUDOS"]<-3
Mothership_Long_dep$TxDay[Mothership_Long_dep$rm=="Day4_CUDOS"]<-4
Mothership_Long_dep$TxDay[Mothership_Long_dep$rm=="Day5_CUDOS"]<-5
Mothership_Long_dep$TxDay[Mothership_Long_dep$rm=="Day6_CUDOS"]<-6
Mothership_Long_dep$TxDay[Mothership_Long_dep$rm=="Day7_CUDOS"]<-7
Mothership_Long_dep$TxDay[Mothership_Long_dep$rm=="Day8_CUDOS"]<-8
Mothership_Long_dep$TxDay[Mothership_Long_dep$rm=="Day9_CUDOS"]<-9
Mothership_Long_dep$TxDay[Mothership_Long_dep$rm=="Day10_CUDOS"]<-10
#Overall race
Mothership_Mean_CUDOS_Race<-Mothership_Long_dep %>%
group_by(TxDay,Race_1) %>%
summarise(mean_CUDOS=mean(CUDOS,na.rm=TRUE))
# plot
ggplot(data=subset(Mothership_Mean_CUDOS_Race,!is.na(Race_1)),
aes(x=TxDay,y=mean_CUDOS,group=Race_1,color=Race_1))+
geom_line(size=1)+
geom_point()+
labs(title ="Depression Severity by Race During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Depression Severity",limits=c(10,40,by=5))+
scale_color_manual(values=c("#209845","#3D4A3E","darkgreen",
"#0098DB","#0065A3","#A0AFA1"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
ggplot(data=subset(Mothership_Mean_CUDOS_Race,!is.na(Race_1)),
aes(x=TxDay,y=mean_CUDOS,group=Race_1,color=Race_1))+
geom_line(size=1)+
geom_point()+
labs(title ="Depression Severity by Race During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Depression Severity",limits=c(10,40,by=5))+
scale_color_manual(values=c("#209845","lightgrey","lightgrey",
"lightgrey","lightgrey","lightgrey"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
ggplot(data=subset(Mothership_Mean_CUDOS_Race,!is.na(Race_1)),
aes(x=TxDay,y=mean_CUDOS,group=Race_1,color=Race_1))+
geom_line(size=1)+
geom_point()+
labs(title ="Depression Severity by Race During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Depression Severity",limits=c(10,40,by=5))+
scale_color_manual(values=c("lightgrey","lightgrey","darkgreen",
"lightgrey","#0065A3","lightgrey"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
GENDER IDENTITY
Create a New Data Set with an Average Depression Variable and Gender, then plot it
#GENDER IDENTITY
#Create a New Data Set with an Average Depression Variable and Gender
Mothership_Mean_CUDOS_Gender<-Mothership_Long_dep %>%
group_by(TxDay,Gender_1) %>%
summarise(mean_CUDOS=mean(CUDOS,na.rm=TRUE))
Mothership_Mean_CUDOS_Gender <-filter(Mothership_Mean_CUDOS_Gender,
Gender_1 != "Unknown",
Gender_1 != "Non-Binary",
Gender_1 != "Other"
)
#Create the Depression Line Graph by Gender
ggplot(data=subset(Mothership_Mean_CUDOS_Gender,!is.na(Gender_1)),
aes(x=TxDay,y=mean_CUDOS,group=Gender_1,color=Gender_1))+
geom_line(size=1)+
geom_point()+
ggtitle("Depression Severity by Gender During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Depression Severity",limits=c(15,45,by=5))+
scale_color_manual(values=c("#209845","#3D4A3E","#A0AFA1",
"#0098DB","#0065A3"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
#### TREATMENT TRACK
#Create a New Data Set with an Average Depression Variable and Track
Mothership_Mean_CUDOS_Track<-Mothership_Long_dep %>%
group_by(TxDay,IPT_track_1) %>%
summarise(mean_CUDOS=mean(CUDOS,na.rm=TRUE))
#Create the Depression Line Graph by Treatment Track
ggplot(data=subset(Mothership_Mean_CUDOS_Track,!is.na(IPT_track_1)),
aes(x=TxDay,y=mean_CUDOS,group=IPT_track_1,color=IPT_track_1))+
geom_line(size=1)+
geom_point()+
ggtitle("Depression Severity by Treatment Track During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Depression Severity",limits=c(20,40,by=5))+
scale_color_manual(values=c("#209845","#3D4A3E","#0065A3"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
EDUCATION
#Create a New Data Set with an Average Depression Variable and Education
Mothership_Mean_CUDOS_Ed<-Mothership_Long_dep %>%
group_by(TxDay,Education_1) %>%
summarise(mean_CUDOS=mean(CUDOS,na.rm=TRUE))
#Create the Depression Line Graph by Education
ggplot(data=subset(Mothership_Mean_CUDOS_Ed,!is.na(Education_1)),
aes(x=TxDay,y=mean_CUDOS,group=Education_1,color=Education_1))+
geom_line(size=.75)+
geom_point(size=.75)+
ggtitle("Depression Severity by Education Level During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Depression Severity",limits=c(20,38,by=5))+
scale_color_manual(values=c("#209845","#3D4A3E","#A0AFA1",
"#0098DB","#0065A3","darkgreen",
"#91A9F1","#7F8559"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
ggplot(data=subset(Mothership_Mean_CUDOS_Ed,!is.na(Education_1)),
aes(x=TxDay,y=mean_CUDOS,group=Education_1,color=Education_1))+
geom_line(size=.75)+
geom_point(size=.75)+
ggtitle("Depression Severity by Education Level During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Depression Severity",limits=c(20,38,by=5))+
scale_color_manual(values=c("#209845","lightgrey","lightgrey",
"#0098DB","lightgrey","lightgrey",
"lightgrey","lightgrey"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
TREATMENT YEAR
#Create a New Data Set with an Average Depression Variable and Year
Mothership_Mean_CUDOS_Year<-Mothership_Long_dep %>%
group_by(TxDay,TxYear) %>%
summarise(mean_CUDOS=mean(CUDOS,na.rm=TRUE))
str(Mothership_Long_dep$TxYear)## Factor w/ 7 levels "2014","2015",..: 1 1 1 1 1 1 1 1 1 1 ...#Create the Depression Line Graph by Year
ggplot(data=subset(Mothership_Mean_CUDOS_Year,!is.na(TxYear)),
aes(x=TxDay,y=mean_CUDOS,group=as.factor(TxYear),color=as.factor(TxYear)))+
geom_line(size=1)+
geom_point()+
ggtitle("Depression Severity by Treatment Year During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Depression Severity",limits=c(20,40,by=5))+
scale_color_manual(values=c("#209845","#3D4A3E","#A0AFA1",
"#0098DB","#91A9F1","darkgreen",
"#0065A3","#7F8559","#4B5D8E"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
#Create the Depression Line Graph by Year
ggplot(data=subset(Mothership_Mean_CUDOS_Year,!is.na(TxYear)),
aes(x=TxDay,y=mean_CUDOS,group=as.factor(TxYear),color=as.factor(TxYear)))+
geom_line(size=1)+
geom_point()+
ggtitle("Depression Severity by Treatment Year During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Depression Severity",limits=c(20,40,by=5))+
scale_color_manual(values=c("lightgrey","lightgrey","lightgrey",
"lightgrey","lightgrey","lightgrey",
"#0065A3","lightgrey","lightgrey"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
Anxiety Treatment Progress Charts
Mothership_Long <- gather(Mothership,rm,CUXOS,Day1_CUXOS:Day40_CUXOS,factor_key = "T")
Mothership_Long <- Mothership_Long[order(Mothership_Long$ID1),]
#Create a Time Variable
Mothership_Long$TxDay <- NA
Mothership_Long$TxDay[Mothership_Long$rm=="Day1_CUXOS"]<-1
Mothership_Long$TxDay[Mothership_Long$rm=="Day2_CUXOS"]<-2
Mothership_Long$TxDay[Mothership_Long$rm=="Day3_CUXOS"]<-3
Mothership_Long$TxDay[Mothership_Long$rm=="Day4_CUXOS"]<-4
Mothership_Long$TxDay[Mothership_Long$rm=="Day5_CUXOS"]<-5
Mothership_Long$TxDay[Mothership_Long$rm=="Day6_CUXOS"]<-6
Mothership_Long$TxDay[Mothership_Long$rm=="Day7_CUXOS"]<-7
Mothership_Long$TxDay[Mothership_Long$rm=="Day8_CUXOS"]<-8
Mothership_Long$TxDay[Mothership_Long$rm=="Day9_CUXOS"]<-9
Mothership_Long$TxDay[Mothership_Long$rm=="Day10_CUXOS"]<-10
Mothership_Long$TxDay[Mothership_Long$rm=="Day11_CUXOS"]<-11
Mothership_Long$TxDay[Mothership_Long$rm=="Day12_CUXOS"]<-12
Mothership_Long$TxDay[Mothership_Long$rm=="Day13_CUXOS"]<-13
Mothership_Long$TxDay[Mothership_Long$rm=="Day14_CUXOS"]<-14
Mothership_Long$TxDay[Mothership_Long$rm=="Day15_CUXOS"]<-15
Mothership_Long$TxDay[Mothership_Long$rm=="Day16_CUXOS"]<-16
Mothership_Long$TxDay[Mothership_Long$rm=="Day17_CUXOS"]<-17
Mothership_Long$TxDay[Mothership_Long$rm=="Day18_CUXOS"]<-18
Mothership_Long$TxDay[Mothership_Long$rm=="Day19_CUXOS"]<-19
Mothership_Long$TxDay[Mothership_Long$rm=="Day20_CUXOS"]<-20
#Create a New Data Set with an Average Anxiety Variable
Mothership_Mean_CUXOS<-Mothership_Long %>%
group_by(TxDay) %>%
summarise(mean_CUXOS=mean(CUXOS,na.rm=TRUE))
# Mothership_Mean_CUXOS20<-Mothership_Long20 %>%
# group_by(TxDay) %>%
# summarise(mean_CUXOS=mean(CUXOS,na.rm=TRUE))#Plot the Anxiety Mean for Full Sample
ggplot(data=Mothership_Mean_CUXOS,aes(x=TxDay,y=mean_CUXOS))+
geom_line(size=1,color="red")+
geom_point(color="red")+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
ggtitle("Mean Anxiety Severity During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20))+
scale_y_continuous(name="Anxiety Severity",limits=c(20,40,by=5))+
theme(panel.background=NULL) 
ggplot(Mothership, aes(Days_complete_1))+
geom_density()+
scale_x_continuous(limits=c(0,20,by=1)) +
labs(
title="Quantity of discharges Per Treatment Day")+
theme_classic()
plot means based upon duration group membership
#plot Anxiety mean with each group
# New dataset for the treatment duration Anxiety plot
Mothership_Mean_CUXOS<-Mothership_Long %>%
group_by(Duration,TxDay) %>%
summarise(mean_CUXOS=mean(CUXOS,na.rm=TRUE))
Mothership_duration_CUXOS<-Mothership_Mean_CUXOS[1:83,]
#delete weird Na values and put NA for values
Mothership_duration_CUXOS[6:20,3] = NA
Mothership_duration_CUXOS[37:41,3] = NA
Mothership_duration_CUXOS[74:83,3] = NA
Mothership_duration_CUXOS<-Mothership_duration_CUXOS[(-21),]
Mothership_duration_CUXOS<-Mothership_duration_CUXOS[(-41),]
Mothership_duration_CUXOS<-Mothership_duration_CUXOS[(-61),]
Mothership_duration_CUXOS## # A tibble: 80 × 3
## # Groups: Duration [4]
## Duration TxDay mean_CUXOS
## <fct> <dbl> <dbl>
## 1 1-5 1 35.4
## 2 1-5 2 27.8
## 3 1-5 3 24.4
## 4 1-5 4 20.6
## 5 1-5 5 9.98
## 6 1-5 6 NA
## 7 1-5 7 NA
## 8 1-5 8 NA
## 9 1-5 9 NA
## 10 1-5 10 NA
## # … with 70 more rowsggplot(data=Mothership_duration_CUXOS,aes(x=TxDay,y=mean_CUXOS,group=Duration,color=Duration))+
geom_line(size=1)+
geom_point()+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
ggtitle("Mean Anxiety Severity During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20))+
scale_y_continuous(name="Anxiety Severity",limits=c(5,50,by=5))+
scale_color_manual(values=c("#BB9D00","red",
"darkred","orange"))+
theme(panel.background=NULL)+
labs(subtitle = "Broken up by Duration of Treatment")
Anxiety Treatment Progress by Characteristics
Race
Mothership<-read.csv("~/Desktop/Coding/data/Mothership_Vis.csv")
Mothership<-mutate(Mothership,
DC_status_1 = as.factor(DC_status_1),
Education_1 = as.factor(Education_1),
Gender_1 = as.factor(Gender_1),
Relationship_1 = as.factor(Relationship_1),
Sexuality_1 = as.factor(Sexuality_1),
IPT_track_1 = as.factor(IPT_track_1),
Race_1 = as.factor(Race_1),
Education_1 = as.factor(Education_1),
Duration = as.factor(Duration),
)
Mothership$Intake_1<-ymd(Mothership$Intake_1,locale = "en_US.UTF-8")
Mothership_Long_anx <- gather(Mothership,rm,CUXOS,Day1_CUXOS:Day40_CUXOS,factor_key = "T")
Mothership_Long_anx <- Mothership_Long_anx[order(Mothership_Long_anx$ID1),]
#Create a Time Variable
Mothership_Long_anx$TxDay <- NA
Mothership_Long_anx$TxDay[Mothership_Long_anx$rm=="Day1_CUXOS"]<-1
Mothership_Long_anx$TxDay[Mothership_Long_anx$rm=="Day2_CUXOS"]<-2
Mothership_Long_anx$TxDay[Mothership_Long_anx$rm=="Day3_CUXOS"]<-3
Mothership_Long_anx$TxDay[Mothership_Long_anx$rm=="Day4_CUXOS"]<-4
Mothership_Long_anx$TxDay[Mothership_Long_anx$rm=="Day5_CUXOS"]<-5
Mothership_Long_anx$TxDay[Mothership_Long_anx$rm=="Day6_CUXOS"]<-6
Mothership_Long_anx$TxDay[Mothership_Long_anx$rm=="Day7_CUXOS"]<-7
Mothership_Long_anx$TxDay[Mothership_Long_anx$rm=="Day8_CUXOS"]<-8
Mothership_Long_anx$TxDay[Mothership_Long_anx$rm=="Day9_CUXOS"]<-9
Mothership_Long_anx$TxDay[Mothership_Long_anx$rm=="Day10_CUXOS"]<-10
#Overall race
Mothership_Mean_CUXOS_Race<-Mothership_Long_anx %>%
group_by(TxDay,Race_1) %>%
summarise(mean_CUXOS=mean(CUXOS,na.rm=TRUE))
Mothership_Mean_CUXOS_Race<-Mothership_Mean_CUXOS_Race[1:70,]
Mothership_Mean_CUXOS_Race## # A tibble: 70 × 3
## # Groups: TxDay [10]
## TxDay Race_1 mean_CUXOS
## <dbl> <fct> <dbl>
## 1 1 Asian 31.2
## 2 1 Black 36.3
## 3 1 Hispanic 43.0
## 4 1 Other 39.7
## 5 1 Portugese 39.2
## 6 1 White 37.5
## 7 1 <NA> 40
## 8 2 Asian 25.7
## 9 2 Black 29.7
## 10 2 Hispanic 36.8
## # … with 60 more rows# plot
ggplot(data=subset(Mothership_Mean_CUXOS_Race,!is.na(Race_1)),
aes(x=TxDay,y=mean_CUXOS,group=Race_1,color=Race_1))+
geom_line(size=1)+
geom_point()+
labs(title ="Anxiety Severity by Race During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Anxiety Severity",limits=c(0,60,by=5))+
scale_color_manual(values=c("#DA2E2E","#57423E",
"gold3","#3E8300",
"#77B81C","darkorange2"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
## GENDER IDENTITY Create a New Data Set with an Average Anxiety
Variable and Gender, then plot it
#GENDER IDENTITY
#Create a New Data Set with an Average Anxiety Variable and Gender
Mothership_Mean_CUXOS_Gender<-Mothership_Long_anx %>%
group_by(TxDay,Gender_1) %>%
summarise(mean_CUXOS=mean(CUXOS,na.rm=TRUE))
Mothership_Mean_CUXOS_Gender <-filter(Mothership_Mean_CUXOS_Gender,
Gender_1 != "Unknown",
Gender_1 != "Non-Binary",
Gender_1 != "Other"
)
#Create the Anxiety Line Graph by Gender
ggplot(data=subset(Mothership_Mean_CUXOS_Gender,!is.na(Gender_1)),
aes(x=TxDay,y=mean_CUXOS,group=Gender_1,color=Gender_1))+
geom_line(size=1)+
geom_point()+
ggtitle("Anxiety Severity by Gender During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Anxiety Severity",limits=c(15,45,by=5))+
scale_color_manual(values=c("#DA2E2E","#57423E"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
TREATMENT TRACK
#Create a New Data Set with an Average Anxiety Variable and Track
Mothership_Mean_CUXOS_Track<-Mothership_Long_anx %>%
group_by(TxDay,IPT_track_1) %>%
summarise(mean_CUXOS=mean(CUXOS,na.rm=TRUE))
#Create the Anxiety Line Graph by Treatment Track
ggplot(data=subset(Mothership_Mean_CUXOS_Track,!is.na(IPT_track_1)),
aes(x=TxDay,y=mean_CUXOS,group=IPT_track_1,color=IPT_track_1))+
geom_line(size=1)+
geom_point()+
ggtitle("Anxiety Severity by Treatment Track During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Anxiety Severity",limits=c(20,46,by=5))+
scale_color_manual(values=c("#DA2E2E","#57423E","gold3"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
### EDUCATION
#Create a New Data Set with an Average Anxiety Variable and Education
Mothership_Mean_CUXOS_Ed<-Mothership_Long_anx %>%
group_by(TxDay,Education_1) %>%
summarise(mean_CUXOS=mean(CUXOS,na.rm=TRUE))
#Create the Anxiety Line Graph by Education
ggplot(data=subset(Mothership_Mean_CUXOS_Ed,!is.na(Education_1)),
aes(x=TxDay,y=mean_CUXOS,group=Education_1,color=Education_1))+
geom_line(size=1)+
geom_point()+
ggtitle("Anxiety Severity by Education Level During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Anxiety Severity",limits=c(20,50,by=5))+
scale_color_manual(values=c("#DA2E2E","#57423E",
"gold3","#3E8300",
"#77B81C","darkorange2",
"#798897","#FD85AE"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)
ggplot(data=subset(Mothership_Mean_CUXOS_Ed,!is.na(Education_1)),
aes(x=TxDay,y=mean_CUXOS,group=Education_1,color=Education_1))+
geom_line(size=1)+
geom_point()+
ggtitle("Anxiety Severity by Education Level During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Anxiety Severity",limits=c(20,50,by=5))+
scale_color_manual(values=c("#DA2E2E","lightgrey","lightgrey",
"gold3","lightgrey","lightgrey",
"lightgrey","lightgrey"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)
TREATMENT YEAR
#Create a New Data Set with an Average Anxiety Variable and Year
Mothership_Mean_CUXOS_Year<-Mothership_Long_anx %>%
group_by(TxDay,TxYear) %>%
summarise(mean_CUXOS=mean(CUXOS,na.rm=TRUE))
#Create the Anxiety Line Graph by Year
ggplot(data=subset(Mothership_Mean_CUXOS_Year,!is.na(TxYear)),
aes(x=TxDay,y=mean_CUXOS,group=as.factor(TxYear),color=as.factor(TxYear)))+
geom_line(size=1)+
geom_point()+
ggtitle("Anxiety Severity by Treatment Year During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Anxiety Severity",limits=c(15,50,by=5))+
scale_color_manual(values=c("#DA2E2E","#57423E",
"gold3","#3E8300",
"#77B81C","darkorange2",
"firebrick4","#FD85AE","#798897"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
ggplot(data=subset(Mothership_Mean_CUXOS_Year,!is.na(TxYear)),
aes(x=TxDay,y=mean_CUXOS,group=as.factor(TxYear),color=as.factor(TxYear)))+
geom_line(size=1)+
geom_point()+
ggtitle("Anxiety Severity by Treatment Year During Partial Hospitalization")+
scale_x_discrete(name="Treatment Day",limits=c(1,2,3,4,5,6,7,8,9,10))+
scale_y_continuous(name="Anxiety Severity",limits=c(15,50,by=5))+
scale_color_manual(values=c("lightgrey","lightgrey",
"lightgrey","lightgrey",
"lightgrey","lightgrey",
"firebrick4","lightgrey","lightgrey"))+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title=element_blank())
RDQ stuff
#Read in a New Data Set
Mothership_RDQ<- read_sav("~/Desktop/Coding/data/Mothership_DV.sav")
Mothership_RDQ<-select(Mothership,ID1:Sexuality_1,MDD_C:Day40_CUXOS)
Mothership_RDQ<-data.frame(sapply(Mothership_RDQ,FUN=as.numeric))
#Make This Data Set Long Data
library(tidyr)
Mothership_Long_RDQ <- gather(Mothership_RDQ,rm,RDQ,rdqPRE_coping:rdqPOST_sym,
factor_key = "T")
Mothership_Long_RDQ <- Mothership_Long_RDQ[order(Mothership_Long_RDQ$ID1),]
view(Mothership_Long_RDQ)
#Create a Time Variable
Mothership_Long_RDQ$PrePost <- NA
Mothership_Long_RDQ$PrePost[Mothership_Long_RDQ$rm=="rdqPRE_coping"]<-"Pre"
Mothership_Long_RDQ$PrePost[Mothership_Long_RDQ$rm=="rdqPRE_pmh"]<-"Pre"
Mothership_Long_RDQ$PrePost[Mothership_Long_RDQ$rm=="rdqPRE_func"]<-"Pre"
Mothership_Long_RDQ$PrePost[Mothership_Long_RDQ$rm=="rdqPRE_wbs"]<-"Pre"
Mothership_Long_RDQ$PrePost[Mothership_Long_RDQ$rm=="rdqPRE_sym"]<-"Pre"
Mothership_Long_RDQ$PrePost[Mothership_Long_RDQ$rm=="rdqPOST_coping"]<-"Post"
Mothership_Long_RDQ$PrePost[Mothership_Long_RDQ$rm=="rdqPOST_pmh"]<-"Post"
Mothership_Long_RDQ$PrePost[Mothership_Long_RDQ$rm=="rdqPOST_func"]<-"Post"
Mothership_Long_RDQ$PrePost[Mothership_Long_RDQ$rm=="rdqPOST_wbs"]<-"Post"
Mothership_Long_RDQ$PrePost[Mothership_Long_RDQ$rm=="rdqPOST_sym"]<-"Post"
#Create a New Data Set with an Average Depression Variable and Race
Mothership_Mean_RDQ<-Mothership_Long_RDQ %>%
group_by(rm,PrePost) %>%
summarise(RDQ=mean(RDQ,na.rm=TRUE))
#Add a Paired Variable to the New Data Set
Mothership_Mean_RDQ<-mutate(Mothership_Mean_RDQ,
paired=case_when(rm=="rdqPRE_coping"~"1",
rm=="rdqPOST_coping"~"1",
rm=="rdqPRE_pmh"~"2",
rm=="rdqPOST_pmh"~"2",
rm=="rdqPRE_func"~"3",
rm=="rdqPOST_func"~"3",
rm=="rdqPRE_wbs"~"4",
rm=="rdqPOST_wbs"~"4",
rm=="rdqPRE_sym"~"5",
rm=="rdqPOST_sym"~"5"))
#Pre Post RDQ Plot
ggplot(data=Mothership_Mean_RDQ,
aes(x=factor(PrePost,level=c("Pre","Post")),y=RDQ,
group=rm,color=paired,!is.na(PrePost)))+
geom_point(size=2.5)+
geom_line(group=Mothership_Mean_RDQ$paired,size=1.5)+
scale_x_discrete(name="Time Point")+
scale_y_continuous(name="RDQ Subscore")+
ggtitle("Mean Pre and Post Scores of the Remission from Depression Questionnaire")+
theme(panel.grid.major.y = element_line(color = "grey",size = 0.5,linetype = 2))+
theme(panel.background=NULL)+
theme(legend.title = element_blank())+
scale_color_manual(values=c("1"="#8956BB","2"="#4C4452","3"="#B1A8B9",
"4"="#00BCA3","5"="#008570"),
labels=c("Coping Skills","Positive Mental Health",
"Functioning","Well-Being","Symptoms"))